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Guangxu Xun
Researcher at University of Virginia
Publications - 48
Citations - 1811
Guangxu Xun is an academic researcher from University of Virginia. The author has contributed to research in topics: Context (language use) & Deep learning. The author has an hindex of 17, co-authored 43 publications receiving 1022 citations. Previous affiliations of Guangxu Xun include University at Buffalo.
Papers
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Proceedings ArticleDOI
MuVAN: A Multi-view Attention Network for Multivariate Temporal Data
TL;DR: Experimental results show that the proposed MuVAN model outperforms the state-of-the-art deep representation approaches in different real-world tasks and can discover discriminative and meaningful attention scores across views over time, which improves the feature representation of multivariate temporal data.
Proceedings ArticleDOI
A novel channel-aware attention framework for multi-channel EEG seizure detection via multi-view deep learning
TL;DR: Experimental results show that the proposed ChannelAtt model outperforms the baselines in detecting epileptic seizures and Analytical results of a case study demonstrate that the learned attentional representations are meaningful.
Journal ArticleDOI
Wave2Vec: Deep representation learning for clinical temporal data
TL;DR: Wave2Vec, an end-to-end deep representation learning model, is proposed to bridge the gap between biosignal processing and semantic learning, and can incorporate both motif co-occurrence information and time series information of biosignals, and hence provides clinically meaningful interpretation.
Proceedings ArticleDOI
Collaboratively Improving Topic Discovery and Word Embeddings by Coordinating Global and Local Contexts
TL;DR: This paper empirically shows that by incorporating both global and local context, this collaborative model can not only significantly improve the performance of topic discovery over the baseline topic models, but also learn better word embeddings than the baseline word embedding models.
Journal ArticleDOI
MeSHProbeNet: a self-attentive probe net for MeSH indexing.
TL;DR: An end-to-end framework, MeSHProbeNet (formerly named as xgx), which utilizes deep learning and self-attentive MeSH probes to index MeSH terms, and achieves the highest scores in all the F-measures.